关键词: 纵向数据, 部分线性变系数EV模型, 二次推断函数

Abstract: Longitudinal data is a kind of correlated data for repeated observation of individuals over time, which is widely used in many scientific research fields. When observing individuals, measurement error is inevitable. Ignoring measurement errors may lead to biased estimation. This paper considers the estimation of the partial linear varying coefficient errors-in-variables (EV) models with longitudinal data using the quadratic inference functions method. We approximate the unknown varying coefficient by B-spline approximations, construct bias-corrected quadratic inference functions about the regression parameter and coefficients of splines to deal with the within-subject correlation and measurement error, get the bias-corrected quadratic inference functions estimation of the regression parameter and varying coefficients, then prove the asymptotic properties of the proposed method and result. Numerical simulation and real data analysis results show that the proposed method has some practical value.

Key words: Longitudinal Data, Partial Linear Varying Coefficient EV Models, Quadratic Inference Functions